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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 4523754, 13 pages
https://doi.org/10.1155/2017/4523754
Research Article

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

School of Computer, Shenyang Aerospace University, Shenyang 110136, China

Correspondence should be addressed to Shoufei Han; moc.liamg@iefuohsnah

Received 29 June 2017; Accepted 3 August 2017; Published 11 September 2017

Academic Editor: Luis Vergara

Copyright © 2017 Xiguang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.